embedding-search
Skill0 starsChanged 3 months agoMITView on GitHub
Use embeddings for semantic search, deduplication, and clustering over a corpus. 用向量嵌入做语义检索、去重与聚类。
In this repository14 other files it gives its agents.
- ai-code-reviewSkill
- ai-cost-estimatorSkill
- ai-news-digestSkill
- ai-paper-highlightsSkill
- ai-tools-radarSkill
- ai-writing-assistantSkill
- fine-tuning-decisionsSkill
- llm-evaluationSkill
- llm-security-checklistSkill
- mcp-server-guideSkill
- model-comparisonSkill
- prompt-engineering-playbookSkill
- rag-pipelineSkill
- structured-output-validationSkill
The file
We split this skill into the rules it gives an agent
1 rule. About 0 are common, 1 is distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Use text embeddings for semantic search, near-duplicate detection, and clustering.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Embedding Search | 向量检索与聚类
Use text embeddings for semantic search, near-duplicate detection, and clustering.
用向量嵌入做语义检索、查重和聚类。
When to use | 适用场景
- 关键词搜索找不准的同义/语义需求
- 给一批文本去重、按主题分组
Workflow | 工作流程
- 选 embedding 模型（open-source 或 API 均可）。
- 把每段文本转成向量。
- 检索：查询向量与库中向量算余弦相似度，取 top-k。
- 去重：相似度 > 阈值（如 0.9）判定为近似重复。
- 聚类：k-means 或 HDBSCAN 按向量分组。
Tips | 提示
- 阈值要先在样本上试，不同领域不一样。
- 向量维度越大越慢，必要时降维（如 PCA）。
Does it work?
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Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.